THE ROBOTS ARE TAKING OVER THE MARKET
Robots are trading millions of stocks by the millisecond, every day. Thanks to ever-advancing algorithms, machines buy and sell in worldwide financial markets at unimaginable speeds.
Robotic trading has only grown in recent years. The most popular form of algorithmic trading, high-frequency trading (HFT), processes historical and real-time market data to know when to buy and sell at lightning speed.
HFT trading volume grew by 164 percent between 2005 and 2009, according to the NYSE. And according to Bank of England, HFTs traded 70 percent of the average daily share volume in US equities.
Thanks to big data, these algorithms are not only becoming smarter at trading billions of dollars every day. The more and more data financial robots consume makes them even smarter at making quick, accurate and profitable investment decisions.
The industry is transforming into a big data business. It’s going to be firms that invest in the technology that will be leaders in the space.
- Ari Rubenstein, co-founder and chief executive at GTS, a high frequency trading company
According to a study from the University of Warwick, the more weekly searches a company has the more it’s traded on the New York Stock Exchange. But trading more of a company’s stock just because it comes up in searches has problems. What if people are searching about their exploding Samsung battery?
This is where sentiment analysis comes in. Algorithms scrape the language millions of people use on Twitter and in Google searches, determining whether people are thinking positively or negatively about a company or product.
Source: Bank of England
These algorithms also watch the sentiment of real-time news coverage. Bad press can trigger machines to sell stocks off in a flurry. And perceived events can affect how algorithms trade on the market as a whole.
CNBC reported the “first real-time demonstration of robo-trading” in 2013 when a hacked AP Twitter account said there were explosions at the White House. Computers abruptly ended trades faster than humans could
It's not so much that the computers initiated trades. What happened is that they canceled the orders, so the bids come out of the market. That causes a crash
A person at an algorithmic trading firm told CNBC.
First, let’s underscore that algorithmic trading isn't just a way to make a profit. It's a way to minimize cost, market impact and risk.
Algo trading is widely used and successful because it replaces human emotions with data analysis. By keeping emotions in check, traders can more easily stick to plan.
Backtesting is another advantage. It applies trading rules to historical market data to determine the viability of an idea. Traders can take these precise sets of rules and test them on historical data before risking money in live trading. This use of algo trading combined with big data minimize risks.
But these methods are controversial due to their role in flash crashes.
Source: Dow Jones Industrial average
On May 6th 2010, the Dow Jones plummeted 1,000 points within a single trading day. Nearly $1 trillion was wiped off the market value, as well as a drop of 600 points within a 5 minute time frame before recovering moments later. The SEC, CFTC and many experts largely blamed HFT firms for the crash.
HFT algorithms worsened the impact of the crash by increasing the price fluctuation. By constantly analyzing the market, they noticed a decline in the stock market value and started to sell vast amounts of securities.
Future systems could study all the historical data archived over the course of the entire trading history, analyze it with ease to find the trends of what could work and what won't.
Most importantly, with a constantly growing amount of data available, it could also teach itself to predict future markets.
Market crashes might become a thing of the past as AI trading improves and realizes the impact of a buy or sell gone wrong.
Imagine a system made powerful through big data and connectivity with rapid internet speed and gigaflops of processing power incorporating structured and unstructured data, utilizing real-time global news feed, with LIVE social media and current and historical stock data across the globe in one algorithmic engine.
A 2010 study from Johan Bollen disclosed that Twitter mood predicts the stock market with 86.7% accuracy. As this research advances, algo trading will use more and more social media, including data we share on social media, to predict how the market will buy or sell securities.